{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../index.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Temperature-dependent QP band structures

\n", "

Carbon in diamond structure

\n", "
\n", "

\n", "In this lesson, we discuss how to compute the electron-phonon (e-ph) self-energy and the renormalization of the electronic states due to the e-ph interaction. We start with a very brief discussion of the basic equations and some technical details about the Abinit implementation of the EPH code. \n", "Then we show how to build an AbiPy flow to automate the multiple steps required by this calculation.\n", "

\n", "

\n", "In the second part, we use the AbiPy objects to analyze the results. We start from the simplest case of a single EPH calculation whose most important results are stored in the SIGEPH.nc file. Then we show how to use SigEPhRobot to handle multiple netcdf files and perform convergence studies. \n", "Finally, we present a possible approach to interpolate the QP corrections to obtain temperature-dependent band structures along an arbitrary high-symmetry k-path.\n", "

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "[[back to top](#top)]\n", "\n", "* [A bit of Theory](#A-bit-of-theory-and-some-details-about-the-implementation)\n", "* [Building a Flow for EPH self-energy calculations](#Building-a-Flow-for-EPH-self-energy-calculations)\n", "* [Electronic properties](#Electronic-properties)\n", "* [Vibrational properties](#Vibrational-properties)\n", "* [E-PH self-energy](#E-PH-self-energy)\n", "* [Using SigEphRobot for convergence studies](#Using-SigEphRobot-for-convergence-studies)\n", "* [Convergence study with respect to nbsum and nqibz](#Convergence-study-with-respect-to-nbsum-and-nqibz)\n", "* [Interpolating QP corrections with star functions](#Interpolating-QP-corrections-with-star-functions)\n", "\n", "\n", "\n", "## A bit of theory and some details about the implementation\n", "[[back to top](#top)]\n", "\n", "$\\newcommand{\\kk}{{\\mathbf k}}$\n", "$\\newcommand{\\qq}{{\\mathbf q}}$\n", "$\\newcommand{\\kpq}{{\\kk+\\qq}}$\n", "$\\newcommand{\\RR}{{\\mathbf R}}$\n", "$\\newcommand{\\rr}{{\\mathbf r}}$\n", "$\\newcommand{\\<}{\\langle}$\n", "$\\renewcommand{\\>}{\\rangle}$\n", "$\\newcommand{\\KS}{{\\text{KS}}}$\n", "$\\newcommand{\\ww}{\\omega}$\n", "$\\newcommand{\\ee}{\\epsilon}$\n", "\n", "The electron-phonon matrix elements are defined by:\n", "\n", "\\begin{equation} %\\label{eq:eph_matrix_elements}\n", " g_{mn}^{\\nu}(\\kk,\\qq) = \\dfrac{1}{\\sqrt{2\\omega_{\\qq\\nu}}} \\<\\psi_{m \\kpq} | \\Delta_{\\qq\\nu} V |\\psi_{n\\kk}\\> \n", "\\end{equation}\n", "\n", "where the perturbation potential, $\\Delta_{\\qq\\nu} V$, is related to the phonon displacement \n", "and the first order derivative of the KS potential:\n", "\n", "\\begin{equation}\n", "\\partial_{\\kappa\\alpha,\\qq} v^{KS} = \\Bigl (\n", "\\partial_{\\kappa\\alpha,\\qq}{v^H} + \n", "\\partial_{\\kappa\\alpha,\\qq}{v^{xc}} +\n", "\\partial_{\\kappa\\alpha,\\qq}{v^{loc}}\n", "\\Bigr ) + \\partial_{\\kappa\\alpha,\\qq}{V^{nl}}\n", "\\ = \\partial_{\\kappa\\alpha,\\qq}{v^{scf}} + \\partial_{\\kappa\\alpha,\\qq}{V^{nl}}\n", "\\end{equation}\n", "\n", "The first term in parentheses depends on the first order change of the density (output of the DFPT run). \n", "The second term derives from the non-local part of the atom-centered (pseudo)potentials\n", "and can be easily computed without having to perform a full DFPT calculation.\n", "Hereafter, we use the term *DFPT potential* to refer to the above expression and\n", "the term *self-consistent DFPT potential* to indicate the contribution that depends on the DFPT density \n", "(the term in parentheses).\n", "\n", "Throughout these notes, we shall use Hartree atomic units ($e = \\hbar = m_e = 1$).\n", "\n", "### EPH self-energy\n", "\n", "The e-ph self-energy consists of two terms (Fan-Migdal + Debye-Waller):\n", "\n", "\\begin{equation}\n", " \\Sigma^{e-ph}(\\ww) = \\Sigma^{FM}(\\ww) + \\Sigma^{DW}\n", "\\end{equation}\n", "\n", "where only the first part (FM) is frequency dependent.\n", "\n", "The *diagonal* matrix elements of the Fan-Migdal self-energy in the KS basis set are given by:\n", "\n", "\\begin{equation}\n", "\\Sigma^{FM}_{n\\kk}(\\ww) = \\sum_{\\qq\\nu m} | g_{nm}^{\\nu}(\\kk, \\qq) | ^ 2 \\times \n", " \\Biggl [\n", " \\dfrac{n_{\\qq\\nu} + f_{m\\kpq}}{\\ww - \\ee_{m\\kpq} - \\ww_{\\qq\\nu} + i\\eta} +\n", " \\dfrac{n_{\\qq\\nu} + 1 - f_{m\\kpq}}{\\ww + \\ee_{m\\kpq} + \\ww_{\\qq\\nu} + i\\eta}\n", "\\Biggr ] \n", "\\end{equation}\n", "\n", "where $n_{\\qq\\nu}$ ($T$) are the temperature-dependent occupation factors for phonons (electrons)\n", "and $\\eta$ is a small positive number (*zcut* input variable).\n", "These occupation factors introduce a dependence on T in the self-energy and therefore in the QP results.\n", "For the sake of simplicity, the dependence on T will not be explicitly mentioned in the other equations.\n", "\n", "The Debye-Waller term is given by:\n", "\n", "\\begin{equation}\n", "\\Sigma_{n\\kk}^{DW} = \\sum_{\\qq\\nu m} (2 n_{\\qq\\nu} + 1) \\dfrac{g_{mn\\nu}^{2,DW}(\\kk, \\qq)}{\\ee_{n\\kk} - \\ee_{m\\kk}}\n", "\\end{equation}\n", "\n", "where $g_{mn\\nu}^{2,DW}(\\kk, \\qq)$ is an effective matrix element that, under the rigid-ion approximation, \n", "can be expressed in terms of the \"standard\" $g_{nm}^{\\nu}(\\kk, \\qq=\\Gamma)$ elements \n", "(see references for a more detailed discussion). \n", "\n", "The QP energies are usually obtained with the linearized QP equation:\n", "\n", "\\begin{equation}\n", "\\ee^{QP}_{n\\kk} \\approx \\ee_{n\\kk} + Z_{n\\kk}\\,\\Sigma^{eph}(\\ee_{n\\kk})\n", "\\end{equation}\n", "\n", "where the renormalization factor $Z$ is given by:\n", "\n", "\\begin{equation}\n", "Z_{n\\kk} = \\Bigl ( 1 - \\Re\\dfrac{\\partial\\Sigma^{eph}}{\\partial{\\ee_{KS}}} \\Bigr )^{-1}\n", "\\end{equation}\n", "\n", "The above expression assumes that the KS states and eigenvalues are relatively close the QP results. \n", "A more rigourous approach would requires solving the non-linear QP equation\n", "in the complex plane with possibly multiple solutions.\n", "\n", "Finally, the spectral function is given by:\n", "\n", "\\begin{equation}\n", "A_{n\\kk}(\\ww) = \\dfrac{1}{\\pi} \\dfrac\n", "{|\\Im \\Sigma^{eph}_{n\\kk}(\\ww)|} \n", "{\\bigl[ \\ww - \\ee_{n\\kk} - \\Re{\\Sigma^{eph}_{n\\kk}(\\ww)} \\bigr ] ^2+ \\Im{\\Sigma^{eph}_{n\\kk}(\\ww)}^2}\n", "\\end{equation}\n", "\n", "\n", "### Interpolation of the DFPT potentials\n", "\n", "EPH calculations require very dense BZ meshes to converge and \n", "the calculation of the DFPT potentials represent a significant fraction of the overall computing time.\n", "To reduce the computational cost, Abinit employs the Fourier-based interpolation technique proposed by\n", "Eiguren and Ambrosch-Draxl in [Phys. Rev. B 78, 045124](http://dx.doi.org/10.1103/PhysRevB.78.045124).\n", "\n", "The DFPT potentials can be interpolated by introducing:\n", "\n", "\\begin{equation}\n", " W(\\rr,\\RR) = \\dfrac{1}{N_\\qq} \\sum_\\qq e^{-i\\qq\\cdot\\RR} \\partial v^{scf}_\\qq(\\rr)\n", "\\end{equation}\n", "\n", "This quantity is usually short-ranged and the *interpolated* potential at an arbitrary point $\\tilde q$\n", "is obtained via:\n", "\n", "\\begin{equation}\n", " V^{scf}_{\\tilde\\qq}(\\rr) = \\sum_\\RR e^{+i{\\tilde \\qq}\\cdot\\RR} W(\\rr, \\RR)\n", "\\end{equation}\n", "\n", "In polar materials, particular care must be taken due to the long-range behavior of $W$.\n", "We will not elaborate on this point further but dynamical quadrupoles are important in diamond.\n", "\n", "\n", "## Technical details\n", "\n", "For the previous discussion, it should be clear that the evaluation of the e-ph self-energy requires:\n", "\n", "1. An initial set of KS wavefunctions and eigenvalues including **several empty** states\n", "\n", "2. The knowledge of the dynamical matrix $D(\\qq)$ from which one \n", " can obtain phonon frequencies and displacements everywhere in the BZ via FT interpolation.\n", " \n", "3. A set of DFPT potentials in the IBZ.\n", "\n", "Thanks to these three ingredients, the EPH code can compute the e-ph matrix elements\n", "and evaluate the self-energy matrix elements.\n", "A schematic representation of a typical EPH workflow for ZPR computations is given in the figure below:\n", "\n", "\n", "\n", "The `mrgddb` and `mrgdv` are Fortran executables whose main goal is to *merge* the partial files\n", "obtained at the end of a single DFPT run for given q-point and atomic perturbation\n", "and produce the final database required by the EPH code.\n", "\n", "Note that, for each quasi-momentum $\\kk$, the self-energy $\\Sigma_{n\\kk}$ \n", "is given by an integral over the BZ.\n", "This means that:\n", "\n", "1. The WFK file must contain the $\\kk$-points and the bands where the QP corrections are wanted as well\n", " as enough empty state to perform the summation over bands.\n", " \n", "2. The code can (Fourier) interpolate the phonon frequencies and the DFPT potentials \n", " over an arbitrarily dense q-mesh. \n", " Still, for fixed $k$-point, the code requires the ab-initio KS states at $\\kk$ and $\\kpq$.\n", " **Providing a WKF file on a k-mesh that fulfills this requirement is responsability of the user**.\n", " \n", "Since we are computing the contribution to the electron self-energy due to the interaction with phonons, \n", "it is not surprising to encounter variables that are also used in the $GW$ code.\n", "More specifically, one can use the `nkptgw`, `kptgw`, `bdgw` variables to select\n", "the electronic states for which the QP corrections are wanted (see also `gw_qprange`) \n", "while `zcut` defines for the complex shift.\n", "If `symsigma` is set to 1, the code takes advantage of symmetries to reduce the BZ integral to an appropriate \n", "irreducible wedge defined by the little group of the k-point (calculations at $\\Gamma$ are therefore\n", "much faster than for other low-symmetry k-points).\n", "\n", "\n", "## Suggested references\n", "[[back to top](#top)]\n", "\n", "You might find additional material, related to the present section, in the following references:\n", "\n", "1. [Temperature dependence of electronic eigenenergies in the adiabatic harmonic approximation](https://doi.org/10.1103/physrevb.90.214304)\n", "2. [Temperature dependence of the electronic structure of semiconductors and insulators](https://aip.scitation.org/doi/abs/10.1063/1.4927081)\n", "3. [Electron-phonon interactions from first principles](https://doi.org/10.1103/RevModPhys.89.015003)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a Flow for EPH self-energy calculations\n", "[[back to top](#top)]\n", "\n", "Before starting, we need to import the python modules used in this notebook:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Use this at the beginning of your script so that your code will be compatible with python3\n", "from __future__ import print_function, division, unicode_literals\n", "\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings(\"ignore\") # to get rid of deprecation warnings\n", "\n", "from abipy import abilab\n", "abilab.enable_notebook() # This line tells AbiPy we are running inside a notebook\n", "import abipy.flowtk as flowtk\n", "\n", "# This line configures matplotlib to show figures embedded in the notebook.\n", "# Replace `inline` with `notebook` in classic notebook\n", "%matplotlib inline \n", "\n", "# Option available in jupyterlab. See https://github.com/matplotlib/jupyter-matplotlib\n", "#%matplotlib widget " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and a function from the `lesson_sigeph` module that will build our AbiPy flow." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from lesson_sigeph import build_flow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Please read the code carefully, in particular the comments.\n", "Don't worry if the meaning of some input variables is not immediately clear\n", "as we will try to clarify the most technical parts in the rest of this notebook.\n", "
" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "

\n", "\n", "
def build_flow(options):\n",
       "    """\n",
       "    C in diamond structure. Very rough q-point mesh, low ecut, completely unconverged.\n",
       "    The flow computes the ground state density and a WFK file on a 8x8x8 k-mesh including\n",
       "    empty states needed for the self-energy. Then all the independent atomic perturbations\n",
       "    for the irreducible qpoints in a 4x4x4 grid are obtained with DFPT.\n",
       "    Finally, we enter the EPH driver to compute the EPH self-energy.\n",
       "    """\n",
       "    workdir = options.workdir if (options and options.workdir) else "flow_diamond"\n",
       "\n",
       "    # Define structure explicitly.\n",
       "    structure = abilab.Structure.from_abivars(\n",
       "        acell=3*[6.70346805],\n",
       "        rprim=[0.0, 0.5, 0.5,\n",
       "               0.5, 0.0, 0.5,\n",
       "               0.5, 0.5, 0.0],\n",
       "        typat=[1, 1],\n",
       "        xred=[0.0, 0.0, 0.0, 0.25, 0.25, 0.25],\n",
       "        ntypat=1,\n",
       "        znucl=6,\n",
       "    )\n",
       "\n",
       "    # Initialize input from structure and norm-conserving pseudo provided by AbiPy.\n",
       "    gs_inp = abilab.AbinitInput(structure, pseudos="6c.pspnc")\n",
       "\n",
       "    # Set basic variables for GS part.\n",
       "    gs_inp.set_vars(\n",
       "        ecut=25.0,          # Too low, shout be ~30\n",
       "        nband=4,\n",
       "        tolvrs=1e-8,\n",
       "    )\n",
       "\n",
       "    # The kpoint grid is minimalistic to keep the calculation manageable.\n",
       "    # The q-mesh for phonons must be a submesh of this one.\n",
       "    gs_inp.set_kmesh(\n",
       "        ngkpt=[8, 8, 8],\n",
       "        shiftk=[0.0, 0.0, 0.0],\n",
       "    )\n",
       "\n",
       "    # Build new input for NSCF calculation with k-path (automatically selected by AbiPy)\n",
       "    # Used to plot the KS band structure and interpolate the QP corrections.\n",
       "    nscf_kpath_inp = gs_inp.new_with_vars(\n",
       "        nband=8,\n",
       "        tolwfr=1e-16,\n",
       "        iscf=-2,\n",
       "    )\n",
       "    nscf_kpath_inp.set_kpath(ndivsm=10)\n",
       "\n",
       "    # Build another NSCF input with k-mesh and empty states.\n",
       "    # This step generates the WFK file used to build the EPH self-energy.\n",
       "    nscf_empty_kmesh_inp = gs_inp.new_with_vars(\n",
       "        nband=210,     # Too low. ~300\n",
       "        nbdbuf=10,     # Reduces considerably the time needed to converge empty states!\n",
       "        tolwfr=1e-16,\n",
       "        iscf=-2,\n",
       "    )\n",
       "\n",
       "    # Create empty flow.\n",
       "    flow = flowtk.Flow(workdir=workdir)\n",
       "\n",
       "    # Register GS + band structure parts in the first work\n",
       "    work0 = flowtk.BandStructureWork(gs_inp, nscf_kpath_inp, dos_inputs=[nscf_empty_kmesh_inp])\n",
       "    flow.register_work(work0)\n",
       "\n",
       "    # Generate Phonon work with 4x4x4 q-mesh\n",
       "    # Reuse variables from GS input and let AbiPy handle the generation of the input files\n",
       "    # Note that the q-point grid is a sub-grid of the k-point grid (here 8x8x8)\n",
       "    ddb_ngqpt = [4, 4, 4]\n",
       "    ph_work = flowtk.PhononWork.from_scf_task(work0[0], ddb_ngqpt, is_ngqpt=True,\n",
       "                                              tolerance={"tolvrs": 1e-6})  # This to speedup DFPT\n",
       "    flow.register_work(ph_work)\n",
       "\n",
       "    # Build template for self-energy calculation. See also v8/Input/t44.in\n",
       "    # The k-points must be in the WFK file\n",
       "    #\n",
       "    eph_inp = gs_inp.new_with_vars(\n",
       "        optdriver=7,             # Enter EPH driver.\n",
       "        eph_task=4,              # Activate computation of EPH self-energy.\n",
       "        ddb_ngqpt=ddb_ngqpt,     # q-mesh used to produce the DDB file (must be consistent with DDB data)\n",
       "        nkptgw=1,\n",
       "        kptgw=[0, 0, 0],\n",
       "        bdgw=[1, 8],\n",
       "        # For more k-points...\n",
       "        #nkptgw=2,\n",
       "        #kptgw=[0, 0, 0, 0.5, 0, 0],\n",
       "        #bdgw=[1, 8, 1, 8],\n",
       "        tmesh=[0, 200, 5],    # (start, step, num)\n",
       "        zcut="0.2 eV",        # Too large. needed to get reasonable results due to coarse q-mesh.\n",
       "        nfreqsp=301,          # Compute A(w)\n",
       "    )\n",
       "\n",
       "    # Set q-path for Fourier interpolation of phonons.\n",
       "    eph_inp.set_qpath(10)\n",
       "\n",
       "    # Set q-mesh for phonons DOS.\n",
       "    eph_inp.set_phdos_qmesh(nqsmall=16, method="tetra")\n",
       "\n",
       "    # EPH part requires the GS WFK, the DDB file with all perturbations\n",
       "    # and the database of DFPT potentials (already merged by PhononWork)\n",
       "    deps = {work0[2]: "WFK", ph_work: ["DDB", "DVDB"]}\n",
       "\n",
       "    # Now we use the EPH template to perform a convergence study in which\n",
       "    # we change the q-mesh used to integrate the self-energy and the number of bands.\n",
       "    # The code will activate the Fourier interpolation of the DFPT potentials if eph_ngqpt_fine != ddb_ngqpt\n",
       "\n",
       "    for eph_ngqpt_fine in [[4, 4, 4], [8, 8, 8]]:\n",
       "        # Create empty work to contain EPH tasks with this value of eph_ngqpt_fine\n",
       "        eph_work = flow.register_work(flowtk.Work())\n",
       "        for nband in [100, 150, 200]:\n",
       "            new_inp = eph_inp.new_with_vars(eph_ngqpt_fine=eph_ngqpt_fine, nband=nband)\n",
       "            eph_work.register_eph_task(new_inp, deps=deps)\n",
       "\n",
       "    flow.allocate()\n",
       "\n",
       "    return flow\n",
       "
\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abilab.print_source(build_flow)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, the function is a little bit long but it is normal as we are computing\n", "in a single workflow the *electronic* properties, the *vibrational* spectrum \n", "and the *electron-phonon* self-energy by varying the number of q-points and the \n", "number of empty states.\n", "\n", "Note that we have already encountered similar flows in the previous AbiPy lessons. \n", "The calculation of electronic band structures is\n", "discussed in \n", "[lesson_base3](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/base3/lesson_base3.ipynb),\n", "an example of `Flow` for phonon calculations is given in \n", "[lesson_dfpt](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/dfpt/lesson_dfpt.ipynb).\n", "An AbiPy Flow for the computation of $G_0W_0$ corrections is discussed in\n", "[lesson_g0w0](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/g0w0/lesson_g0w0.ipynb).\n", "\n", "The novelty is represented by the generation of the `EphTasks` \n", "in which we have to specify several variables related to phonons and the e-ph self-energy.\n", "For your convenience, we have grouped the variables used in the `EphTask` in sub-groups:\n", "\n", "\n", "\n", "Hopefully things will become clearer if we build the flow and start to play with it:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Number of works: 4, total number of tasks: 23\n", "Number of tasks with a given class:\n", "\n", "Task Class Number\n", "------------ --------\n", "ScfTask 1\n", "NscfTask 2\n", "PhononTask 14\n", "EphTask 6" ] } ], "source": [ "flow = build_flow(options=None)\n", "flow.show_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now print the input of the last `EphTask`. \n", "Please read carefully the documentation of the variables, in particular \n", "those in the `vargw`, `varrf` and `vareph` sections.\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "##############################################
#### SECTION: basic
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##############################################
#### SECTION: eph
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ddb_ngqpt 4 4 4
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ph_ndivsm 10
ph_nqpath 12
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#### SECTION: gstate
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#### STRUCTURE
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3.3517340250 3.3517340250 0.0000000000" ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(flow[-1][-1])\n", "flow[-1][-1].input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "The input does not contain any `irdwfk` or `getwfk` variable. AbiPy will add the required `ird*` \n", "variables at runtime and create symbolic links to connect this task to its parents.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, it is much easier to understand what is happening if we plot a graph\n", "with the individual tasks and their connections.\n", "Since there are several nodes in our graph, we first focus on the EPH part \n", "and then we \"zoom-out\" to unveil the entire workflow.\n", "Let's have a look at the parents of the last `EphTask` with:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "task\n", "\n", "EphTask, node_id=242751, workdir=flow_diamond/w3/t2\n", "clusterw3\n", "\n", "EphTask (w3_t2)\n", "\n", "\n", "w0_t2\n", "\n", "w0_t2\n", "NscfTask\n", "\n", "\n", "w3_t2\n", "\n", "w3_t2\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w1\n", "\n", "w1\n", "PhononWork\n", "\n", "\n", "w1->w3_t2\n", "\n", "\n", "DDB+DVDB\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[-1][-1].get_graphviz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph shows that the `EphTask` uses the `WFK` produced by a NSCF calculation \n", "as well as two other files generated by the `PhononWork`. \n", "If this is not the first time you run DFPT calculations with Abinit, you already know that the `DDB`\n", "is essentially a text file with the independent entries of the dynamical matrix.\n", "This file is usually produced by merging partial `DDB` files with the `mrgddb` utility.\n", "For further information about the `DDB` see [this notebook](../ddb.ipynb).\n", "\n", "The `DVDB`, on the other hand, is a Fortran binary file containing an independent set\n", "of DFPT potentials (actually the first-order variation of the self-consistent KS potential\n", "due to an atomic perturbation characterized by a q-point and the [`idir, ipert`] pair).\n", "The `DVDB` is generated by merging the `POT` files produced at the end of the DFPT run\n", "with the `mrgdv` tool.\n", "\n", "Now let's turn our attention to the last `Work`:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "work\n", "\n", "Work, node_id=242748, workdir=flow_diamond/w3\n", "clusterw3\n", "\n", "Work (w3)\n", "\n", "\n", "w0_t2\n", "\n", "w0_t2\n", "NscfTask\n", "\n", "\n", "w3_t0\n", "\n", "w3_t0\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w3_t1\n", "\n", "w3_t1\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w3_t2\n", "\n", "w3_t2\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w1\n", "\n", "w1\n", "PhononWork\n", "\n", "\n", "w1->w3_t0\n", "\n", "\n", "DDB+DVDB\n", "\n", "\n", "w1->w3_t1\n", "\n", "\n", "DDB+DVDB\n", "\n", "\n", "w1->w3_t2\n", "\n", "\n", "DDB+DVDB\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[-1].get_graphviz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, this section of the `flow` consists of three `EphTasks`, all with the same parents.\n", "This is the part that computes the e-ph self-energy with a fixed q-mesh and different values of `nband`.\n", "We can easily check this with:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nband: [100, 150, 200]\n", "q-mesh for self-energy: [[8, 8, 8], [8, 8, 8], [8, 8, 8]]\n" ] } ], "source": [ "print(\"nband:\", [task.input[\"nband\"] for task in flow[-1]])\n", "print(\"q-mesh for self-energy:\", [task.input[\"eph_ngqpt_fine\"] for task in flow[-1]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or, alternatvely, we select the last work and call `get_vars_dataframe` with the list of variable names:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nbandeph_ngqpt_fineclass
w3_t0100[8, 8, 8]EphTask
w3_t1150[8, 8, 8]EphTask
w3_t2200[8, 8, 8]EphTask
\n", "
" ], "text/plain": [ " nband eph_ngqpt_fine class\n", "w3_t0 100 [8, 8, 8] EphTask\n", "w3_t1 150 [8, 8, 8] EphTask\n", "w3_t2 200 [8, 8, 8] EphTask" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[-1].get_vars_dataframe(\"nband\", \"eph_ngqpt_fine\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is another `work` made of `EphTasks` with similar organization, the only difference being\n", "that we use the ab-initio q-mesh for the DFPT potentials (no Fourier interpolation here). " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nband: [100, 150, 200]\n", "q-mesh for self-energy: [[4, 4, 4], [4, 4, 4], [4, 4, 4]]\n" ] } ], "source": [ "print(\"nband:\", [task.input[\"nband\"] for task in flow[-2]])\n", "print(\"q-mesh for self-energy:\", [task.input[\"eph_ngqpt_fine\"] for task in flow[-2]])" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nbandeph_ngqpt_fineclass
w2_t0100[4, 4, 4]EphTask
w2_t1150[4, 4, 4]EphTask
w2_t2200[4, 4, 4]EphTask
\n", "
" ], "text/plain": [ " nband eph_ngqpt_fine class\n", "w2_t0 100 [4, 4, 4] EphTask\n", "w2_t1 150 [4, 4, 4] EphTask\n", "w2_t2 200 [4, 4, 4] EphTask" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[-2].get_vars_dataframe(\"nband\", \"eph_ngqpt_fine\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we should have a good understanding of the input files required by the `EphTasks`.\n", "There is only one point missing! How do we get the `WFK`, `DDB`, `DVDB` files?\n", "\n", "Well, in the old days we used to write several input files one for each q-point in the IBZ, link files \n", "and merge the intermediate results manually but now we can use python to automate the entire procedure:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "flow\n", "\n", "Flow, node_id=242715, workdir=flow_diamond\n", "clusterw0\n", "\n", "BandStructureWork (w0)\n", "\n", "clusterw1\n", "\n", "PhononWork (w1)\n", "\n", "clusterw2\n", "\n", "Work (w2)\n", "\n", "clusterw3\n", "\n", "Work (w3)\n", "\n", "\n", "w0_t0\n", "\n", "w0_t0\n", "ScfTask\n", "\n", "\n", "w0_t1\n", "\n", "w0_t1\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t1\n", "\n", "\n", "DEN\n", "\n", "\n", "w0_t2\n", "\n", "w0_t2\n", "NscfTask\n", "\n", "\n", "w0_t0->w0_t2\n", "\n", "\n", "DEN\n", "\n", "\n", "w1_t0\n", "\n", "w1_t0\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t1\n", "\n", "w1_t1\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t2\n", "\n", "w1_t2\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t3\n", "\n", "w1_t3\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t3\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t4\n", "\n", "w1_t4\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t4\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t5\n", "\n", "w1_t5\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t5\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t6\n", "\n", "w1_t6\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t6\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t7\n", "\n", "w1_t7\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t7\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t8\n", "\n", "w1_t8\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t8\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t9\n", "\n", "w1_t9\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t9\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t10\n", "\n", "w1_t10\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t10\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t11\n", "\n", "w1_t11\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t11\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t12\n", "\n", "w1_t12\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t12\n", "\n", "\n", "WFK\n", "\n", "\n", "w1_t13\n", "\n", "w1_t13\n", "PhononTask\n", "\n", "\n", "w0_t0->w1_t13\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t0\n", "\n", "w2_t0\n", "EphTask\n", "\n", "\n", "w0_t2->w2_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t1\n", "\n", "w2_t1\n", "EphTask\n", "\n", "\n", "w0_t2->w2_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w2_t2\n", "\n", "w2_t2\n", "EphTask\n", "\n", "\n", "w0_t2->w2_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "w3_t0\n", "\n", "w3_t0\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t0\n", "\n", "\n", "WFK\n", "\n", "\n", "w3_t1\n", "\n", "w3_t1\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t1\n", "\n", "\n", "WFK\n", "\n", "\n", "w3_t2\n", "\n", "w3_t2\n", "EphTask\n", "\n", "\n", "w0_t2->w3_t2\n", "\n", "\n", "WFK\n", "\n", "\n", "DDB (w1)\n", "\n", "DDB (w1)\n", "\n", "\n", "DDB (w1)->w2_t0\n", "\n", "\n", "\n", "\n", "DDB (w1)->w2_t1\n", "\n", "\n", "\n", "\n", "DDB (w1)->w2_t2\n", "\n", "\n", "\n", "\n", "DDB (w1)->w3_t0\n", "\n", "\n", "\n", "\n", "DDB (w1)->w3_t1\n", "\n", "\n", "\n", "\n", "DDB (w1)->w3_t2\n", "\n", "\n", "\n", "\n", "DVDB (w1)\n", "\n", "DVDB (w1)\n", "\n", "\n", "DVDB (w1)->w2_t0\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w2_t1\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w2_t2\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w3_t0\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w3_t1\n", "\n", "\n", "\n", "\n", "DVDB (w1)->w3_t2\n", "\n", "\n", "\n", "\n", "\n", "__0:clusterw1->DDB (w1)\n", "\n", "\n", "\n", "\n", "\n", "__1:clusterw1->DVDB (w1)\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow.get_graphviz()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", "Remember that the $q$-point mesh cannot be chosen arbitrarily\n", "since all $q$ wavevectors should connect two $k$ points of the grid used for the electrons.\n", "\n", "More specifically, the q-mesh associated to the \n", "`DDB` (`DVDB`) must be a submesh of the k-mesh for electrons.\n", "Moreover the EPH code can compute self-energy corrections only for the states that are available in the `WFK` file.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can generate the directories and the input files of the `Flow` by executing the \n", "`lesson_sigeph.py` script that will generate the flow_diamond directory with all the input \n", "files required by the calculation.\n", "\n", "Then use the `abirun.py` script to launch the calculation with the scheduler:\n", "\n", " abirun.py flow_diamond scheduler\n", " \n", "You may want to run this example in the terminal if you have already installed and configured AbiPy and Abinit\n", "on your machine.\n", "The calculation requires ~8 minutes on a poor 1.7 GHz Intel Core i5 (2 minutes for all the EPH tasks,\n", "the rest for the DFPT section).\n", "\n", "If you prefer to skip this part, jump to next section in which we focus on the post-processing of the results.\n", "Note that the output files are already available in the [github repository](https://github.com/abinit/abitutorials)\n", "so it is possible to play with the AbiPy post-processing tools without having to run the flow.\n", "In particular, one can use the command line and the commands:\n", "\n", " abiopen.py FILE\n", " \n", "to open the file inside ipython,\n", "\n", " abiopen.py out_SIGEPH.nc --expose\n", " \n", "to visualize the self-energy results and finally,\n", "\n", " abicomp.py sigeph flow_diamond/w3/\n", " \n", "to compare multiple `SIGEPH.nc` files with the robot inside ipython." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Electronic properties\n", "[[back to top](#top)]\n", "\n", "Let's focus on the electronic properties first." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_diamond//w0/t2/outdata/out_GSR.nc\r\n", "flow_diamond//w0/t1/outdata/out_GSR.nc\r\n", "flow_diamond//w0/t0/outdata/out_GSR.nc\r\n" ] } ], "source": [ "!find flow_diamond/ -name \"out_GSR.nc\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The task `w0/t0` computed the electronic band structure on a high-symmetry k-path.\n", "Let's plot the bands with:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "with abilab.abiopen(\"flow_diamond/w0/t1/outdata/out_GSR.nc\") as gsr:\n", " ebands_kpath = gsr.ebands\n", " \n", "ebands_kpath.plot(with_gaps=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not surprising to see that diamond is a indirect gap semiconductor. \n", "The KS band structure gives a direct gap at $\\Gamma$ of ~5.6 eV while the fundamental gap \n", "is ~4.2 eV (VBM at $\\Gamma$, CBM at ~[+0.357, +0.000, +0.357] in reduced coordinates).\n", "These values have been computer by ApiPy on the basis of the k-path employed to compute the band structure\n", "in the `NscfTask`:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= Structure =================================\n", "Full Formula (C2)\n", "Reduced Formula: C\n", "abc : 2.508336 2.508336 2.508336\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (2)\n", " # SP a b c cartesian_forces\n", "--- ---- ---- ---- ---- -----------------------------------------------------------\n", " 0 C 0 0 0 [5.14220675e+101 5.14220675e+101 5.14220675e+101] eV ang^-1\n", " 1 C 0.25 0.25 0.25 [5.14220675e+101 5.14220675e+101 5.14220675e+101] eV ang^-1\n", "\n", "Abinit Spacegroup: spgid: 227, num_spatial_symmetries: 48, has_timerev: True, symmorphic: True\n", "\n", "Number of electrons: 8.0, Fermi level: 12.743 (eV)\n", "nsppol: 1, nkpt: 198, mband: 8, nspinor: 1, nspden: 1\n", "smearing scheme: none (occopt 1), tsmear_eV: 0.272\n", "Direct gap:\n", " Energy: 5.617 (eV)\n", " Initial state: spin: 0, kpt: [+0.000, +0.000, +0.000], name: $\\Gamma$, weight: 0.000, band: 3, eig: 12.743, occ: 2.000\n", " Final state: spin: 0, kpt: [+0.000, +0.000, +0.000], name: $\\Gamma$, weight: 0.000, band: 4, eig: 18.361, occ: 0.000\n", "Fundamental gap:\n", " Energy: 4.205 (eV)\n", " Initial state: spin: 0, kpt: [+0.000, +0.000, +0.000], name: $\\Gamma$, weight: 0.000, band: 3, eig: 12.743, occ: 2.000\n", " Final state: spin: 0, kpt: [+0.357, +0.000, +0.357], weight: 0.000, band: 4, eig: 16.949, occ: 0.000\n", "Bandwidth: 21.540 (eV)\n", "Valence maximum located at:\n", " spin: 0, kpt: [+0.000, +0.000, +0.000], name: $\\Gamma$, weight: 0.000, band: 3, eig: 12.743, occ: 2.000\n", "Conduction minimum located at:\n", " spin: 0, kpt: [+0.357, +0.000, +0.357], weight: 0.000, band: 4, eig: 16.949, occ: 0.000\n", "\n", "TIP: Call set_fermie_to_vbm() to set the Fermi level to the VBM if this is a non-magnetic semiconductor\n", "\n" ] } ], "source": [ "print(ebands_kpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the `NscfTask` `w0/t2` we have computed the KS eigenstates including several empty states. \n", "This is the `WFK` file we are going to use to build the `EPH` self-energy.\n", "\n", "Let's plot the bands in the IBZ and the associated DOS with:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "with abilab.abiopen(\"flow_diamond/w0/t2/outdata/out_GSR.nc\") as gsr:\n", " ebands_4sigma = gsr.ebands\n", "\n", "# 8x8x8 is too coarse to get nice DOS so we increase the gaussian smearing\n", "# (we are mainly interested in the possible presence of ghost states --> sharp peaks) \n", "ebands_4sigma.plot_with_edos(ebands_4sigma.get_edos(width=0.6));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure shows that our value of `nband` correspond to ~400 eV above the valence band maximum (VBM).\n", "\n", "Note that it is always a good idea to look at the behaviour of the high-energy states\n", "when running self-energy calculations, especially the first time you use a new pseudopotential.\n", "\n", "**Ghost states**, indeed, can appear somewhere in the high-energy region causing sharp peaks in the DOS.\n", "The presence of these high-energy ghosts is not clearly seen in standard GS calculations \n", "but they can have drammatic consequences for many-body calculations based on sum-over-states approaches.\n", "\n", "This is one of the reasons why the pseudopotentials of the [pseudo-dojo project](http://www.pseudo-dojo.org/)\n", "have been explicitly tested for the presence of ghost states in the empty region\n", "(see [here](https://www.sciencedirect.com/science/article/pii/S0010465518300250?via%3Dihub) for further details)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vibrational properties\n", "[[back to top](#top)]\n", "\n", "Now we turn our attention to the vibrational properties.\n", "AbiPy has already merged all the independent atomic perturbations in `flow_diamond/w1/outdata/out_DDB`:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_diamond//w0/t0/outdata/out_DDB\r\n", "flow_diamond//w1/t11/outdata/out_DDB\r\n", "flow_diamond//w1/t10/outdata/out_DDB\r\n", "flow_diamond//w1/t5/outdata/out_DDB\r\n", "flow_diamond//w1/t2/outdata/out_DDB\r\n", "flow_diamond//w1/t3/outdata/out_DDB\r\n", "flow_diamond//w1/t4/outdata/out_DDB\r\n", "flow_diamond//w1/t12/outdata/out_DDB\r\n", "flow_diamond//w1/t13/outdata/out_DDB\r\n", "flow_diamond//w1/outdata/out_DDB\r\n", "flow_diamond//w1/t1/outdata/out_DDB\r\n", "flow_diamond//w1/t6/outdata/out_DDB\r\n", "flow_diamond//w1/t8/outdata/out_DDB\r\n", "flow_diamond//w1/t9/outdata/out_DDB\r\n", "flow_diamond//w1/t7/outdata/out_DDB\r\n", "flow_diamond//w1/t0/outdata/out_DDB\r\n" ] } ], "source": [ "!find flow_diamond/ -name \"out_DDB\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the input file for `mrgddb` produced automatically by python:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/outdata/out_DDB\r\n", "DDB file merged by PhononWork on Wed Sep 30 03:53:31 2020\r\n", "14\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t0/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t1/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t2/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t3/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t4/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t5/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t6/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t7/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t8/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t9/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t10/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t11/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t12/outdata/out_DDB\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t13/outdata/out_DDB\r\n" ] } ], "source": [ "!cat flow_diamond//w1/outdata/mrgddb.stdin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first line gives the name of the output DDB file followed by a title, the number of partial DDB files\n", "we want to merge and their path.\n", "\n", "A similar input file must be provided to `mrgdv`, the only difference is that DDB files are not replaced \n", "by `POT*` files with the SCF part of the DFPT potential (Warning: Unlike the out_DDB files, \n", "the out_DVDB file and the associated mrgdvdb.stdin files are available \n", "in the present tutorial only if you have actually issued `abirun.py flow_diamond scheduler` above. \n", "Still, the files needed beyond the two next commands are available by default, so that you can continue to examine this tutorial.)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "flow_diamond//w1/outdata/out_DVDB\r\n" ] } ], "source": [ "!find flow_diamond/ -name \"out_DVDB\"" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/outdata/out_DVDB\r\n", "14\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t0/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t1/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t2/outdata/out_POT2\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t3/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t4/outdata/out_POT2\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t5/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t6/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t7/outdata/out_POT2\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t8/outdata/out_POT3\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t9/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t10/outdata/out_POT3\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t11/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t12/outdata/out_POT1\r\n", "/Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/t13/outdata/out_POT2\r\n" ] } ], "source": [ "!cat flow_diamond//w1/outdata/mrgdvdb.stdin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's open the `DDB` file with:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= File Info =================================\n", "Name: out_DDB\n", "Directory: /Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w1/outdata\n", "Size: 55.08 kb\n", "Access Time: Wed Sep 30 04:00:47 2020\n", "Modification Time: Wed Sep 30 03:53:31 2020\n", "Change Time: Wed Sep 30 03:53:31 2020\n", "\n", "================================= Structure =================================\n", "Full Formula (C2)\n", "Reduced Formula: C\n", "abc : 2.508336 2.508336 2.508336\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (2)\n", " # SP a b c\n", "--- ---- ---- ---- ----\n", " 0 C 0 0 0\n", " 1 C 0.25 0.25 0.25\n", "\n", "Abinit Spacegroup: spgid: 0, num_spatial_symmetries: 48, has_timerev: True, symmorphic: True\n", "\n", "================================== DDB Info ==================================\n", "\n", "Number of q-points in DDB: 8\n", "guessed_ngqpt: [4 4 4] (guess for the q-mesh divisions made by AbiPy)\n", "ecut = 25.000000, ecutsm = 0.000000, nkpt = 260, nsym = 48, usepaw = 0\n", "nsppol 1, nspinor 1, nspden 1, ixc = 1, occopt = 1, tsmear = 0.010000\n", "\n", "Has total energy: False, Has forces: False\n", "Has stress tensor: False\n", "\n", "Has (at least one) atomic pertubation: True\n", "Has (at least one diagonal) electric-field perturbation: False\n", "Has (at least one) Born effective charge: False\n", "Has (all) strain terms: False\n", "Has (all) internal strain terms: False\n", "Has (all) piezoelectric terms: False\n" ] } ], "source": [ "ddb = abilab.abiopen(\"flow_diamond/w1/outdata/out_DDB\")\n", "print(ddb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then invoke `anaddb` to compute phonon bands and DOS:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "phbst, phdos = ddb.anaget_phbst_and_phdos_files()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we plot the results with:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "phbst.phbands.plot_with_phdos(phdos);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The maximum phonon frequency is ~0.17 eV that is much smaller that the KS fundamental gap.\n", "This means that at T=0 we cannot have scattering processes \n", "between two electronic states in which only one phonon is involved. \n", "This however does not mean that the electronic properties will not be renormalized by the EPH interaction, even at T=0\n", "(zero-point motion effect).\n", "\n", "So far, we managed to generate a `WFK` with empty states on a 8x8x8 k-mesh, \n", "and a pair of `DDB`-`DVDB` files on a 4x4x4 q-mesh.\n", "We can now finally turn our attention to the EPH self-energy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## E-PH self-energy\n", "[[back to top](#top)]\n", "\n", "Let's focus on the output files produced by the first `EphTask` in `w2/t0`:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "out_EBANDS.agr out_PHBANDS.agr out_PHDOS.nc\r\n", "out_OUT.nc out_PHBST.nc out_SIGEPH.nc\r\n" ] } ], "source": [ "!ls flow_diamond/w2/t0/outdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where:\n", " \n", "* *out_PHBST.nc*: phonon band structure along the q-path\n", "* *out_PHDOS.nc*: phonon DOS and projections over atoms and directions\n", "* *out_SIGEPH.nc*: $\\Sigma^{eph}_{nk\\sigma}(T)$ matrix elements for different temperatures $T$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several physical properties stored in the `SIGEPH` file.\n", "As usual, we can use `abiopen` to open the file and `print(abifile)` to get a quick summary \n", "of the most important results.\n", "\n", "Let's open the file obtained with the 8x8x8 q-mesh and 200 bands (our best calculation):" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================= File Info =================================\n", "Name: out_SIGEPH.nc\n", "Directory: /Users/gmatteo/git_repos/abitutorials/abitutorials/sigeph/flow_diamond/w3/t2/outdata\n", "Size: 498.37 kb\n", "Access Time: Wed Sep 30 04:00:53 2020\n", "Modification Time: Wed Sep 30 03:53:56 2020\n", "Change Time: Wed Sep 30 03:53:56 2020\n", "\n", "================================= Structure =================================\n", "Full Formula (C2)\n", "Reduced Formula: C\n", "abc : 2.508336 2.508336 2.508336\n", "angles: 60.000000 60.000000 60.000000\n", "Sites (2)\n", " # SP a b c\n", "--- ---- ---- ---- ----\n", " 0 C 0 0 0\n", " 1 C 0.25 0.25 0.25\n", "\n", "Abinit Spacegroup: spgid: 0, num_spatial_symmetries: 48, has_timerev: True, symmorphic: True\n", "\n", "============================= KS Electron Bands =============================\n", "Number of electrons: 8.0, Fermi level: 12.743 (eV)\n", "nsppol: 1, nkpt: 29, mband: 210, nspinor: 1, nspden: 1\n", "smearing scheme: none (occopt 1), tsmear_eV: 0.272\n", "Direct gap:\n", " Energy: 5.617 (eV)\n", " Initial state: spin: 0, kpt: [+0.000, +0.000, +0.000], weight: 0.002, band: 3, eig: 12.743, occ: 2.000\n", " Final state: spin: 0, kpt: [+0.000, +0.000, +0.000], weight: 0.002, band: 4, eig: 18.361, occ: 0.000\n", "Fundamental gap:\n", " Energy: 4.209 (eV)\n", " Initial state: spin: 0, kpt: [+0.000, +0.000, +0.000], weight: 0.002, band: 3, eig: 12.743, occ: 2.000\n", " Final state: spin: 0, kpt: [+0.375, +0.375, +0.000], weight: 0.012, band: 4, eig: 16.952, occ: 0.000\n", "Bandwidth: 21.540 (eV)\n", "Valence maximum located at:\n", " spin: 0, kpt: [+0.000, +0.000, +0.000], weight: 0.002, band: 3, eig: 12.743, occ: 2.000\n", "Conduction minimum located at:\n", " spin: 0, kpt: [+0.375, +0.375, +0.000], weight: 0.012, band: 4, eig: 16.952, occ: 0.000\n", "\n", "TIP: Call set_fermie_to_vbm() to set the Fermi level to the VBM if this is a non-magnetic semiconductor\n", "\n", "\n", "============================ SigmaEPh calculation ============================\n", "Calculation type: Real + Imaginary part of SigmaEPh\n", "Number of k-points in Sigma_{nk}: 1\n", "sigma_ngkpt: [0 0 0], sigma_erange: [0. 0.]\n", "Max bstart: 0, min bstop: 8\n", "Initial ab-initio q-mesh:\n", "\tngqpt: [8 8 8], with nqibz: 29\n", "q-mesh for self-energy integration (eph_ngqpt_fine): [8 8 8]\n", "k-mesh for electrons:\n", "\tmpdivs: [8 8 8] with shifts [[0. 0. 0.]] and kptopt: 1\n", "Number of bands included in e-ph self-energy sum: 200\n", "zcut: 0.00735 (Ha), 0.200 (eV)\n", "Number of temperatures: 5, from 0.0 to 800.0 (K)\n", "symsigma: 1\n", "Has Eliashberg function: False\n", "Has Spectral function: True\n", "\n", "Printing QP results for 2 temperatures. Use --verbose to print all results.\n", "\n", "KS, QP (Z factor) and on-the-mass-shell (OTMS) direct gaps in eV for T = 0.0 K:\n", " Spin k-point KS_gap QPZ_gap QPZ - KS OTMS_gap OTMS - KS\n", "------ --------------------------------- -------- --------- ---------- ---------- -----------\n", " 0 [+0.000, +0.000, +0.000] $\\Gamma$ 5.617 5.322 -0.295 5.264 -0.353\n", "\n", "\n", "KS, QP (Z factor) and on-the-mass-shell (OTMS) direct gaps in eV for T = 800.0 K:\n", " Spin k-point KS_gap QPZ_gap QPZ - KS OTMS_gap OTMS - KS\n", "------ --------------------------------- -------- --------- ---------- ---------- -----------\n", " 0 [+0.000, +0.000, +0.000] $\\Gamma$ 5.617 5.243 -0.374 5.146 -0.471\n", "\n" ] } ], "source": [ "#sigeph = abilab.abiopen(\"flow_diamond/w2/t0/outdata/out_SIGEPH.nc\")\n", "sigeph = abilab.abiopen(\"flow_diamond/w3/t2/outdata/out_SIGEPH.nc\")\n", "print(sigeph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output reveals that the direct QP gaps are smaller \n", "than the corresponding KS values, even at $T=0$.\n", "Still it is difficult to understand what is happening without a *graphical* representation of the results.\n", "Let's use matplotlib to plot the **difference** between the QP and the KS direct gap at $\\Gamma$ as a function or T:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sigeph.plot_qpgaps_t();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In diamond, the EPH interaction at T=0 decreases the direct gap at $\\Gamma$ with respect \n", "to the KS value. Moreover the QP gap decreases with T.\n", "This behaviour, however, cannot be generalized in the sense that there are systems \n", "in which the direct gap *increases* with T." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far we have been focusing on the QP direct gaps but we can also look \n", "at the QP results for the CBM and the VBM:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Valence band max\n", "sigeph.plot_qpdata_t(spin=0, kpoint=0, band_list=[3]);" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Conduction band min\n", "sigeph.plot_qpdata_t(spin=0, kpoint=0, band_list=[4]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how the real part of the self-energy at the VBM goes up with T while the CBM goes down with T,\n", "thus explaining the behavior of the QP gap vs T observed previously (well one should also include $Z$ but\n", "the real part gives the most important contribution...)\n", "\n", "We do not discuss the results for the imaginary part because these values requires a very dense \n", "sampling of the BZ to give reasonable results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the real and the imaginary part of $\\Sigma_{nk}^{eph}(\\omega)$ \n", "and the spectral function $A_{nk}(\\omega)$ obtained for the different temperatures with:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sigma = sigeph.get_sigeph_skb(spin=0, kpoint=[0, 0, 0], band=3)\n", "sigma.plot_tdep(xlims=[-1, 2]);" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sigeph.get_sigeph_skb(spin=0, kpoint=[0, 0, 0], band=4).plot_tdep(xlims=(-1, 2));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Converging these quantities is not an easy task since they are very sensitive to the q-point sampling.\n", "Still, we can see that the spectral function has a dominant peak that is shifted with respect \n", "to the initial KS energy. The spectral function of the CBM has more structures than the VBM..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In some cases, we need to access the raw data. \n", "For example we may want to build a table with the QP results for all bands at fixed k-point, spin.\n", "The code below creates a pandas `DataFrame` and selects only the entries at 0 K." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bande0re_qpeqpeme0re_sig0imag_sig0ze0re_fan0dwtmesh
00-8.796107-8.857742-0.061635-0.0627450.00.982315-0.1210610.0583160.0
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0312.74347412.8663240.1228500.1313240.00.935473-0.9369211.0682450.0
0418.36058318.188235-0.172348-0.2221560.00.775797-1.1664130.9442570.0
0518.36058318.188235-0.172348-0.2221560.00.775797-1.1664130.9442570.0
0618.36058318.188235-0.172348-0.2221560.00.775797-1.1664130.9442570.0
0726.67250026.6753280.0028280.0044610.00.633845-0.0805220.0849830.0
\n", "
" ], "text/plain": [ " band e0 re_qpe qpeme0 re_sig0 imag_sig0 ze0 \\\n", "0 0 -8.796107 -8.857742 -0.061635 -0.062745 0.0 0.982315 \n", "0 1 12.743474 12.866324 0.122850 0.131324 0.0 0.935473 \n", "0 2 12.743474 12.866324 0.122850 0.131324 0.0 0.935473 \n", "0 3 12.743474 12.866324 0.122850 0.131324 0.0 0.935473 \n", "0 4 18.360583 18.188235 -0.172348 -0.222156 0.0 0.775797 \n", "0 5 18.360583 18.188235 -0.172348 -0.222156 0.0 0.775797 \n", "0 6 18.360583 18.188235 -0.172348 -0.222156 0.0 0.775797 \n", "0 7 26.672500 26.675328 0.002828 0.004461 0.0 0.633845 \n", "\n", " re_fan0 dw tmesh \n", "0 -0.121061 0.058316 0.0 \n", "0 -0.936921 1.068245 0.0 \n", "0 -0.936921 1.068245 0.0 \n", "0 -0.936921 1.068245 0.0 \n", "0 -1.166413 0.944257 0.0 \n", "0 -1.166413 0.944257 0.0 \n", "0 -1.166413 0.944257 0.0 \n", "0 -0.080522 0.084983 0.0 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = sigeph.get_dataframe_sk(spin=0, kpoint=[0, 0, 0], ignore_imag=True)\n", "df[df[\"tmesh\"] == 0.0]" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# To plot the QP results as a function of the KS energy:\n", "#sigeph.plot_qps_vs_e0();" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "# To plot KS bands and QP results:\n", "#sigeph.plot_qpbands_ibzt();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using SigEphRobot for convergence studies\n", "[[back to top](#top)]\n", "\n", "Our initial goal was to analyze the convergence of the QP corrections as a function of `nbsum`.\n", "This means that we should now collect multiple `SIGEPH.nc` files, extract the data,\n", "order the results and visualize them.\n", "Fortunately, we can use the `SigEphRobot` (or the `abicomp.py` script) \n", "to automate the most boring and repetitive operations.\n", "\n", "The code below, for instance, tells our robot to open all the `SIGEPH` files \n", "located within `flow_diamond/w2` (fixed q-mesh, different `nband`).\n", "The syntax used to specify multiple directories should be familiar to Linux users:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "
  1. flow_diamond/w2/t2/outdata/out_SIGEPH.nc
  2. \n", "
  3. flow_diamond/w2/t1/outdata/out_SIGEPH.nc
  4. \n", "
  5. flow_diamond/w2/t0/outdata/out_SIGEPH.nc
  6. \n", "
" ], "text/plain": [ "Label Relpath\n", "---------------------------------------- ----------------------------------------\n", "flow_diamond/w2/t2/outdata/out_SIGEPH.nc flow_diamond/w2/t2/outdata/out_SIGEPH.nc\n", "flow_diamond/w2/t1/outdata/out_SIGEPH.nc flow_diamond/w2/t1/outdata/out_SIGEPH.nc\n", "flow_diamond/w2/t0/outdata/out_SIGEPH.nc flow_diamond/w2/t0/outdata/out_SIGEPH.nc" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_bsum = abilab.SigEPhRobot.from_dir_glob(\"flow_diamond/w2/t*/outdata/\")\n", "robot_bsum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's change the labels of the files by replacing file paths with strings constructed\n", "from `nbsum`:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('nbsum: 200', 'flow_diamond/w2/t2/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 150', 'flow_diamond/w2/t1/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 100', 'flow_diamond/w2/t0/outdata/out_SIGEPH.nc')])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_bsum.remap_labels(lambda sigeph: \"nbsum: %d\" % sigeph.nbsum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and check that everything is OK by printing the convergence parameters: " ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nbsumzcutsymsigmanqbznqibzddb_nqbzeph_nqbz_fineph_nqbzeph_intmetheph_fsewineph_fsmeareph_extraeleph_fermie
nbsum: 2002000.00734986507959246516486464409610.040.010.00.0
nbsum: 1501500.00734986507959246516486464409610.040.010.00.0
nbsum: 1001000.00734986507959246516486464409610.040.010.00.0
\n", "
" ], "text/plain": [ " nbsum zcut symsigma nqbz nqibz ddb_nqbz \\\n", "nbsum: 200 200 0.007349865079592465 1 64 8 64 \n", "nbsum: 150 150 0.007349865079592465 1 64 8 64 \n", "nbsum: 100 100 0.007349865079592465 1 64 8 64 \n", "\n", " eph_nqbz_fine ph_nqbz eph_intmeth eph_fsewin eph_fsmear \\\n", "nbsum: 200 64 4096 1 0.04 0.01 \n", "nbsum: 150 64 4096 1 0.04 0.01 \n", "nbsum: 100 64 4096 1 0.04 0.01 \n", "\n", " eph_extrael eph_fermie \n", "nbsum: 200 0.0 0.0 \n", "nbsum: 150 0.0 0.0 \n", "nbsum: 100 0.0 0.0 " ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_bsum.get_params_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the only parameter that is changing in this set of netcdf files is `nbsum`.\n", "Now all the files are in memory and we can start our convergence study.\n", "\n", "
\n", "There are several quantities that depend on T thus complicating a bit the analysis.\n", "So we use the `itemp` index to select the first temperature. \n", "
\n", "\n", "To plot the convergence of QP gaps vs the `nbsum` parameter, use:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "robot_bsum.plot_qpgaps_convergence(sortby=\"nbsum\", itemp=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The convergence of the QP gaps versus `nbsum` is not variational \n", "and very slow: the same run with 20 bands (not shown here) would give completely different results.\n", "Note that QP gaps (energy differences) are usually easier to converge than absolute QP energies.\n", "A similar behaviour is observed in $GW$ calculations as well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also analyze the convergence of the individual QP terms for given $(\\sigma, {\\bf{k}}, n)$ with:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "robot_bsum.plot_qpdata_conv_skb(spin=0, kpoint=(0, 0, 0), band=3, sortby=\"nbsum\");" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "#robot_bsum.plot_qpdata_conv_skb(spin=0, kpoint=(0, 0, 0), band=4, sortby=\"nbsum\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and perform a similar analysis for $\\Sigma^{eph}_{n{\\bf k}}(\\omega)$ \n", "and $A_{n{\\bf k}}(\\omega)$:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "robot_bsum.plot_selfenergy_conv(spin=0, kpoint=(0 , 0, 0), band=4, itemp=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how the largest variations are observed in the real part of the self-energy \n", "while the imaginary part is rather insensitive to the number of empty states. Why?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convergence study with respect to nbsum and nqibz\n", "[[back to top](#top)]\n", "\n", "In the previous section, we have analyzed the convergence of the QP results as a function of `nbsum` \n", "for a fixed q-point mesh.\n", "The main conclusion was that the convergence with respect to the number of bands is slow\n", "and that even 200 bands are not enough to converge the QP gap.\n", "\n", "Here we use *all* the `SIGEPH.nc` files produced by the `flow` to study \n", "how the final results depend on `nbsum` and `nqibz`.\n", "\n", "The main goal is to understand whether these two parameters are correlated or not.\n", "In other words we want to understand whether we really need to perform a convergence study in which\n", "both parameters are increased or whether we can assume some sort of *weak correlation* that allows \n", "us to *decouple* the two convergence studies.\n", "\n", "Let's start by loading all the `SIGPEH.nc` files located inside `flow_diamond` with:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "
  1. w3/t2/outdata/out_SIGEPH.nc
  2. \n", "
  3. w3/t1/outdata/out_SIGEPH.nc
  4. \n", "
  5. w3/t0/outdata/out_SIGEPH.nc
  6. \n", "
  7. w2/t2/outdata/out_SIGEPH.nc
  8. \n", "
  9. w2/t1/outdata/out_SIGEPH.nc
  10. \n", "
  11. w2/t0/outdata/out_SIGEPH.nc
  12. \n", "
" ], "text/plain": [ "Label Relpath\n", "--------------------------- ----------------------------------------\n", "w3/t2/outdata/out_SIGEPH.nc flow_diamond/w3/t2/outdata/out_SIGEPH.nc\n", "w3/t1/outdata/out_SIGEPH.nc flow_diamond/w3/t1/outdata/out_SIGEPH.nc\n", "w3/t0/outdata/out_SIGEPH.nc flow_diamond/w3/t0/outdata/out_SIGEPH.nc\n", "w2/t2/outdata/out_SIGEPH.nc flow_diamond/w2/t2/outdata/out_SIGEPH.nc\n", "w2/t1/outdata/out_SIGEPH.nc flow_diamond/w2/t1/outdata/out_SIGEPH.nc\n", "w2/t0/outdata/out_SIGEPH.nc flow_diamond/w2/t0/outdata/out_SIGEPH.nc" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_qb = abilab.SigEPhRobot.from_dir(\"flow_diamond\")\n", "robot_qb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and replace the labels with:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('nbsum: 200, nqibz: 29', 'w3/t2/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 150, nqibz: 29', 'w3/t1/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 100, nqibz: 29', 'w3/t0/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 200, nqibz: 8', 'w2/t2/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 150, nqibz: 8', 'w2/t1/outdata/out_SIGEPH.nc'),\n", " ('nbsum: 100, nqibz: 8', 'w2/t0/outdata/out_SIGEPH.nc')])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_qb.remap_labels(lambda sigeph: \"nbsum: %d, nqibz: %d\" % (sigeph.nbsum, sigeph.nqibz))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the calculations in `w2` are done with a 4x4x4 q-mesh, while in `w3` we have EPH calculations\n", "done with a 8x8x8 q-mesh. \n", "Let's print a table with the parameters of the run, just to be sure..." ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nbsumzcutsymsigmanqbznqibzddb_nqbzeph_nqbz_fineph_nqbzeph_intmetheph_fsewineph_fsmeareph_extraeleph_fermie
nbsum: 200, nqibz: 292000.00734986507959246515122964512409610.040.010.00.0
nbsum: 150, nqibz: 291500.00734986507959246515122964512409610.040.010.00.0
nbsum: 100, nqibz: 291000.00734986507959246515122964512409610.040.010.00.0
nbsum: 200, nqibz: 82000.00734986507959246516486464409610.040.010.00.0
nbsum: 150, nqibz: 81500.00734986507959246516486464409610.040.010.00.0
nbsum: 100, nqibz: 81000.00734986507959246516486464409610.040.010.00.0
\n", "
" ], "text/plain": [ " nbsum zcut symsigma nqbz nqibz \\\n", "nbsum: 200, nqibz: 29 200 0.007349865079592465 1 512 29 \n", "nbsum: 150, nqibz: 29 150 0.007349865079592465 1 512 29 \n", "nbsum: 100, nqibz: 29 100 0.007349865079592465 1 512 29 \n", "nbsum: 200, nqibz: 8 200 0.007349865079592465 1 64 8 \n", "nbsum: 150, nqibz: 8 150 0.007349865079592465 1 64 8 \n", "nbsum: 100, nqibz: 8 100 0.007349865079592465 1 64 8 \n", "\n", " ddb_nqbz eph_nqbz_fine ph_nqbz eph_intmeth \\\n", "nbsum: 200, nqibz: 29 64 512 4096 1 \n", "nbsum: 150, nqibz: 29 64 512 4096 1 \n", "nbsum: 100, nqibz: 29 64 512 4096 1 \n", "nbsum: 200, nqibz: 8 64 64 4096 1 \n", "nbsum: 150, nqibz: 8 64 64 4096 1 \n", "nbsum: 100, nqibz: 8 64 64 4096 1 \n", "\n", " eph_fsewin eph_fsmear eph_extrael eph_fermie \n", "nbsum: 200, nqibz: 29 0.04 0.01 0.0 0.0 \n", "nbsum: 150, nqibz: 29 0.04 0.01 0.0 0.0 \n", "nbsum: 100, nqibz: 29 0.04 0.01 0.0 0.0 \n", "nbsum: 200, nqibz: 8 0.04 0.01 0.0 0.0 \n", "nbsum: 150, nqibz: 8 0.04 0.01 0.0 0.0 \n", "nbsum: 100, nqibz: 8 0.04 0.01 0.0 0.0 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "robot_qb.get_params_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can start to analyze the data but there is a small problem because, strictly speaking,\n", "we are dealing with a function of two variables. (`nbsum` and `nqibz`).\n", "Let's analyze the convergence of the QP direct gap as a function of `nbsum` and use the `hue` argument\n", "to group results obtained with different q-meshes:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "robot_qb.plot_qpgaps_convergence(sortby=\"nbsum\", hue=\"nqibz\", itemp=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure reveals that the largest variation is due to the q-point sampling,\n", "nonetheless the two curves as function of `nbsum` have very similar trend \n", "(they are just converging to different values) \n", "This suggests that one could simplify considerably the convergence study by decoupling the two parameters.\n", "In particular, one could perform an initial convergence study with respect to `nbsum` with a relatively coarse q-mesh\n", "to find a reasonable value then *fix* `nbsum` and move to the convergence study with respect to the q-point sampling\n", "and the zcut broadening.\n", "\n", "A similar approach has been recently used by \n", "[van Setten](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.155207)\n", "to automate $GW$ calculation." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_qpfield_vs_e0(\"qpeme0\", itemp=0, sortby=\"nbsum\", hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_qpdata_conv_skb(spin=0, kpoint=(0, 0, 0), band=3, sortby=\"nbsum\", hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_selfenergy_conv(spin=0, kpoint=(0.5, 0, 0), band=4, itemp=0, sortby=\"nbsum\", hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_qpgaps_t(); #sortby=\"nbsum\", plot_qpmks=True, hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "run_control": { "marked": true }, "scrolled": false }, "outputs": [], "source": [ "#robot_qb.plot_qpgaps_t(sortby=\"nbsum\", plot_qpmks=True, hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_qpgaps_convergence(itemp=0, sortby=\"nbsum\", hue=\"nqibz\");" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "#robot_qb.plot_qpfield_vs_e0(\"ze0\", itemp=0, sortby=\"nbsum\", hue=\"nqibz\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolating QP corrections with star-functions\n", "[[back to top](#top)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous paragraphs, we discussed how to compute the QP corrections as function of $T$ \n", "for a set of k-points in the IBZ. \n", "In many cases, however, we would like to visualize the effect of the self-energy \n", "on the *electronic dispersion* using a high-symmetry k-path.\n", "There are several routes to address this problem.\n", "\n", "The most accurate method would be performing multiple self-energy calculations \n", "with different shifted k-meshes so as to sample the high-symmetry k-path \n", "and then merging the results.\n", "Alternatively, one could try to *interpolate* the QP energies obtained in the IBZ.\n", "\n", "It is worth noting that the QP energies must fulfill the symmetry properties of the point group of the crystal:\n", "\n", "\\begin{equation}\n", "\\epsilon({\\bf k + G}) = \\epsilon({\\bf k})\n", "\\end{equation}\n", "\n", "and \n", "\n", "\\begin{equation}\n", "\\epsilon(\\mathcal O {\\bf k }) = \\epsilon({\\bf k})\n", "\\end{equation}\n", "\n", "where $\\bf{G}$ is a reciprocal lattice vector and $\\mathcal{O}$ is an operation of the point group.\n", "\n", "Therefore it is possible to employ the star-function interpolation by Shankland, Koelling and Wood\n", "in the improved version proposed by [Pickett](http://dx.doi.org/10.1103/physrevb.38.2721)\n", "to fit the ab-initio results.\n", "This interpolation technique, by construction, passes through the initial points and \n", "satisfies the basic symmetry property of the band energies.\n", "\n", "It should be stressed, however, that this Fourier-based method can have problems in the presence of band crossings \n", "that may cause unphysical oscillations between the ab-initio points.\n", "To reduce this spurious effect, we prefer to interpolate the QP corrections instead of the QP energies.\n", "The corrections, indeed, are usually smoother in k-space and the resulting fit is more stable.\n", "\n", "To interpolate the QP corrections we have to provide a SIGEPH file with the QP energies computed\n", "in the entire irreducible zone.\n", "A netcdf file obtained with `nband == 100` is already provided in the github repository.\n", "Note that the QP corrections in this case have been obtained by setting $Z = 1$" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "sigeph_ibz = abilab.abiopen(\"ibz_SIGEPH.nc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the QP energies and the KS bands in the irreducible wedge:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sigeph_ibz.plot_qpbands_ibzt();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we extract the KS bands along the k-path from the GSR file produced by the second NscfTask" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "with abilab.abiopen(\"flow_diamond/w0/t1/outdata/out_GSR.nc\") as gsr:\n", " ks_ebands_kpath = gsr.ebands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we **interpolate the QP corrections** for the first two temperatures by calling:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31msigres.structure and ks_ebands_kpath.structures differ. Check your files!\u001b[0m\n", "Using: 145 star-functions. nstars/nk: 5.0\n", "\u001b[32mFIT vs input data: Mean Absolute Error= 2.927e-11 (meV)\u001b[0m\n", "Using: 145 star-functions. nstars/nk: 5.0\n", "\u001b[32mFIT vs input data: Mean Absolute Error= 0.000e+00 (meV)\u001b[0m\n", "Using: 145 star-functions. nstars/nk: 5.0\n", "\u001b[32mFIT vs input data: Mean Absolute Error= 3.161e-11 (meV)\u001b[0m\n", "Using: 145 star-functions. nstars/nk: 5.0\n", "\u001b[32mFIT vs input data: Mean Absolute Error= 0.000e+00 (meV)\u001b[0m\n" ] } ], "source": [ "tdep = sigeph_ibz.interpolate(ks_ebands_kpath=ks_ebands_kpath, itemp_list=[0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "The small value of the MAE indicates that the fit passes through the ab-initio points (as expected).\n", "This, however, does not exclude the possibility of unphysical oscillations between the input data points.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, one can plot the interpolated QP bands and the ab-initio KS dispersion with:" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tdep.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's zoom in on the region around the gap:" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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